Although according to a 2020 McKinsey study1, 50% of the companies surveyed had already adopted AI in at least one business function, the state of AI in 2023 according to a similar McKinsey study suggests that adoption rates have effectively plateaued over the last 3 years2.On the other hand, in the same survey, 2/3rds of the respondents expected their respective organizations to increase AI adoption in the next 3 years.
For example, Generative AI has garnered widespread interest since OpenAI’s ChatGPT launch in early 2023, with several studies, including McKinsey’s 2023 state of AI report2, suggesting that this may well be Generative AI’s breakout year. Organizations around the world are already seeing immense value in Gen AI and have now started to explore other areas of adoption as well.
With this context, conversations around the importance of AI/ML platforms to get AI applications up and running seem inevitable. AI/ML platforms can potentially accelerate the entire lifecycle from model training and preparation, to deployment and integration.
A critical decision each organization must make is the choice of an AI/ML platform. This decision can significantly influence the success that companies have in rolling out their AI initiatives.
Organizations look at AI/ML platforms as a means to take care of the non-differentiated heavy lifting involved with AI applications. The use of an AI/ML platform eases the process of developing ML models and AI applications. As the focus for each enterprise should be on the machine learning models, insights and applications within their problem domain, AI/ML platforms can be seen as enabling infrastructure.
AI/ML platforms enable the efficient development and deployment of AI applications in collaborative environments using the latest advancements in machine learning.
The importance of using an AI/ML platform
When AI/ML platforms are implemented effectively, they can reduce operational costs, improve productivity and help grow revenues. Because AI/ML platforms can solve for the infrastructure problem, the data-to-decisions journey is shorter with patterns and trends identified more quickly and more intuitively from within the data. Business leaders must select the best platform to create and operationalize ML models and AI applications at speed and scale to stay competitive in the market.
Management of the entire ML model lifecycle
Machine learning platforms support each step of the model lifecycle, starting with data provisioning. Platforms often have data discovery mechanisms and connectors that make it easy to feed data into the machine models. A few years ago, data sets were used to train models, and then later, data feeds were established for use in production. Now, data pipelines can be created that include any necessary pre-processing steps. Data enrichment is still needed, coupled with any essential translation, formatting or quality control measures defined as part of the pipeline.
Typically, AI solutions require many iterations before the final version is ready for production. Many training runs may be necessary, and testing on real-world data is critical. As multiple roles working together is critical for AI success, collaboration is also supported by AI/ML platforms. Data scientists and business analysts are heavily involved in the training and preparation phases, as well as post-deployment analysis. The platforms also handle the deployment of models, which requires coordination with application developers and IT operations. There are numerous handoffs needed throughout the process and these well-crafted platforms are designed to facilitate seamless transitions that speed up the lifecycle.
As multiple stakeholders are involved in this process, including engineers, analysts, and data scientists, the ability to rapidly gather feedback and incorporate it into the development process is vital for feature velocity. This is where AI/ML platforms shine and show their worth, as they rapidly facilitate team development and model deployment.
Another key advantage of AI/ML platforms is that organizations are not tied to a single framework or model implementation. Platform vendors make it easy to leverage multiple frameworks and introduce new ones over time quickly. Different models can run on various frameworks simultaneously. They all can easily co-exist using the platform. The latest updates can be applied as well so that organizations can quickly take advantage of the latest innovations. Platforms often include tooling that sits on top of the frameworks, making it easier to build models and leverage new features for customers.
Deploy AI applications, not just models
Use cases often involve integration with other enterprise applications and the delivery of data, intelligence, and insights to stakeholders. The knowledge and insights produced by AI/ML technology are only useful once the appropriate stakeholders can leverage them. Thus, AI/ML platforms have evolved in scope to the point where they can be used to build entire applications with minimal coding efforts.
Custom software development is no longer a requirement to make the capabilities available to all stakeholders. Many platforms can now be used to create user interfaces, applications and workflows on top of their machine learning capabilities. The tools offered by these platforms provide the ability to orchestrate multiple models into overarching workflows. The result is no longer just machine learning components but low-code/no-code AI solutions ready to be deployed.
Governance and monitoring capabilities are also built into the platforms to ensure applications perform as expected. ML models can be adjusted as needed, and continuous deployment mechanisms make it easy to roll out updates. Decision-making has become a continuous process for many organizations;therefore, real-time insights are critical to the success of the business. Data streams are constantly being analyzed and processed, and AI/ML platforms enable teams to act on this feedback loop and iterate quickly. Sharing analysis and insights is enabled using dashboards, charts and other integration mechanisms.
Choosing the right AI/ML platform
Now that we understand the importance of AI/ML platforms, we can appreciate the need for choosing the right AI/ML platform for your business. Machine learning models are at the heart of AI applications and AI/ML platforms provide the tooling to build, deploy and manage these ML models.
Forrester published in their 2022 report “Now Tech: AI/ML Platforms” that vendors provide tooling in three main product/service designs. These include:
1. Multimodal vendors
Multimodal vendors provide various user interface mechanisms, including machine learning tools such as visual data pipeline builders. Data visualization and analysis capabilities are also offered using visual mechanisms. A benefit of this approach is that team members do not require coding skills to use the tools.
2. Code-first vendors
Code-first vendors believe that programming languages are the preferred mechanism used to build and manage machine learning models. These platforms often focus heavily on using open-source notebooks such as Jupyter. Visual tooling in these products is typically oriented around the coding environment used to implement the capabilities.
AI-as-a-service vendors offer AI models that are ready to use. Data Scientists can use these artificial intelligence services individually or in combination to add AI functionality directly to their applications.